基于 CEEMDAN 和概率神经网络的 起伏振动气液两相流型识别

起伏振动气液两相流型准确识别对漂浮核动力平台安全稳定运行有重要意义。通过对比静止和起伏振动管道的压差信号以及对应的频谱图发现,起伏振动管道内的压差信号波动幅度更大且包含更多的频率分量,两种流型均含有主频率,该频率为起伏振动频率。针对起伏振动状态气液两相流压差信号的复杂性,分别采用自适应白噪声的完备总体经验模态分解(CEEMDAN)和集合经验模态分解(EEMD)对小波降噪后的压差信号进行模式分解,发现CEEMDAN能够在减少模式分量的同时获得更多有效的分量。通过计算spearman相关系数选择具有表征意义的IMF分量进行Hilbert变换计算能量作为特征值,采用概率神经网络对流型进行识别。结果表明,采用CEEMDAN进行模式分解结合概率神经网络的识别方法准确率达到95.83%,能够用于起伏振动下气液两相流型识别。

Flow pattern identification of fluctuating vibration gas liquid two phase flow based on CEEMDAN and probabilistic neural network

Accurate identification of fluctuating vibration gas-liquid two-phase flow pattern is of great significance for the safe and stable operation of nuclear power platform under floating vibration. Through comparing the differential pressure signals and the corresponding spectrums in static and fluctuating vibration pipelines, it is found that the differential pressure signals in fluctuating vibration pipelines have larger fluctuation amplitude and contain more frequency components, and both flow patterns have dominant frequency, which is the fluctuating vibration frequency. Aiming at the complexity of the pressure difference signals of gas-liquid two-phase flow in fluctuating vibration state, complete ensemble empirical mode decomposition with adaptive noise ( CEEMDAN) and ensemble empirical mode decomposition (EEMD) are used to decompose the pressure difference signals after wavelet de-noising. It is found that CEEMDAN can reduce the mode components and obtain more effective components at the same time. Through calculating the Spearman correlation coefficient, the IMF components that have symbolic meaning are selected to perform Hilbert transform, and the energy is calculated and used as the eigenvalue. Probabilistic neural network is used to identify the flow pattern. The results show that using CEEMDAN to perform mode decomposition, combining with probabilistic neural network, the accuracy of the identification method is 95. 83% , and this method can be used to identify the flow pattern of gas-liquid two-phase flow under fluctuating vibration.